Imagine that a hedge fund approaches you with an enticing proposition: they’ve earned average annual returns in excess of 35% over a 30-year period that saw multiple recessions, stock market crashes and other calamities… and they want you to invest in their new fund.

To put that number in perspective, a 35% average annual return means that $1,000 invested 30 years ago would be worth $8.1 million today.

So how much would you pay in management fees and carry for the “privilege” of investing in such a great fund?

The answer: 5 and 44.

Yes, Renaissance Technologies is well-known for charging a management fee of 5% and carry of 44%, way above the industry average of 2% and 20%… because they’re just that good.

And this scenario is not just in your imagination: their premier fund has actually delivered 35% average annual returns after fees over 30 years (good luck getting an invitation to invest, though).

It’s no coincidence that Renaissance is also a quant hedge fund – the type that employs a small army of mathematicians, physicists, statisticians, and PhD’s all with a singular purpose: make as much money as humanly possible.

Our interviewee today also works at a quant hedge fund, and while he can’t detail his specific trading strategies due to the secrecy of the industry, he will share with you a bunch of insights on the space, including:

How to make sense of the quant fund space and what types of positions may be available

What an average day in your life will be like at a quant fund

How much money you’ll make at the entry-level, and as you move up the ladder

What prerequisites you need to win interviews at a quant fund, and what kinds of questions you might get in a quant interview

Let’s get started with “quantifying” all of this…

Quantifying Quant Funds & Predicting Positions

Q: I know you don’t want to share too many details of your own story breaking into the industry, so let’s start with an industry overview instead. What is a “quant hedge fund” and what are the different categories of quant funds?

A: Essentially, it’s any fund that relies on statistical techniques and math modeling rather than fundamental analysis to make investments.

A quant hedge fund, on the other hand, would say, “Here are 200 possible conditions that might result in the stock price of Company X rising. Let’s test the statistical significance of all those conditions and use them to predict the direction that the stock is headed in.”

Less Mathematically Complex Assets:Equities and anything else where the effective yield can’t be stated with a formula.

The quants spend a lot of time calculating metrics like cash flow and arbitrage-free valuation with the first one; the second asset class is less complex, but funds will still use statistical techniques to predict price movements.

Then you can divide the strategies into high-frequency and low-frequency; the former requires strong IT infrastructure, while the latter demands strong math modeling.

A: Good quant hedge funds typically treat trading questions as “scientific questions”: for example, what is the probability that MSFT will increase tomorrow?

To answer this question, a fund would study the conditions that made MSFT rise vs. fall historically. They would quantify these conditions and test the statistical significance of their conclusions. Examples:

Over the past 10 years, whenever Company X stock rose by 10%, MSFT declined by 5%; there’s a 65% correlation between the two.

Over the past 5 years, whenever trading volume of MSFT exceeded Y, it increased by X% on average; there’s a 50% correlation between the two.

In the past year, whenever oil exceeded $Z per barrel, MSFT declined by X% on average; there’s a 70% correlation between the two.

So you think about conditions like these, determine the significance and correlations between all of them, and then come up with an overall model that tells you whether an asset such as a stock will increase or decrease in value.

Q: So it’s definitely a different kind of modeling than what bankers do – almost like a series of predictive statistical experiments.

What exactly do high frequency funds trade on? And how are they structured?

A: Their strategy is typically a variant of the following: “I know that somebody else will buy X, so let’s buy X first and sell it to them at a higher price.”

Some high frequency funds build algorithms to detect large orders that are about to be executed and front-run them accordingly. So in contrast to low-frequency funds, the high-frequency ones focus more on predicting these types of orders in advance rather than testing for the statistical significance of conditions before trading.

In terms of structure, there are traditional high-frequency funds that work the same way as any other hedge fund or quant fund, but there are also firms that are like “hotels for hedge funds.”

In other words, the company has a number of small teams that operate independently of each other. These teams may or may not be trading the same assets or strategies, and the company and the team members split the revenue. Examples of such firms are Tudor, Millennium, SAC, and Tower.

Each smaller fund has its own PM, team, and track record, but they will follow certain guidelines put together by the parent fund.

Q: I see – so what about the internal structure at those places? Do people specialize or is it more of an ad hoc structure?

A: It depends on the firm – I don’t think you can generalize. At some places, each employee performs a specialized task and “plugs in” his output into a large, automated system. For example, some employees may forecast returns while others will model transaction costs.

Renaissance works a bit like that where the roles and responsibilities are very structured; Citadel is an example of a fund where it’s more ad hoc and people do whatever is most useful at the moment.

Q: Speaking of these specific funds, any thoughts on the big names in the industry and how they all differ from each other?

A: Honestly, most of these places guard information so closely that no one outside really knows what goes on; my comments above are all I know about how some of these funds work.

A: Those roles still exist, but you see a few additional positions as well. At most quant funds, the 3 main categories of junior-level employees are:

Traders: Similar to what traders anywhere else do: they execute trades by finding willing buyers and sellers and sometimes also come up with ideas on their own; relationships withbrokers are critical here. You see mostly former traders from bulge bracket banks and occasionally from other hedge funds here.

Quants: They build tools to help traders assess potential rewards and risks of trades, or they try to predict asset returns. This is where you see the small army of statisticians, physicists, and mathematicians.

Programmers: They develop data access and analytical tools; they have to understand not only the markets and trading strategies, but also how all the software and hardware works, and they must write clean, extensible, and robust code because the software changes all the time.

A: The importance of a human trader increases as assets that people trade become more complex. It decreases as more information becomes quantifiable and people better understand how to price and predict these assets.

So far, the second trend is winning. But there is probably some fundamental lower bound on the importance of traders, because there will always be “un-quantifiable information.”

I think the outlook is probably slightly better for you if you’re interested in joining a quant fund in more of a quant or programmer role, but there will always be demand for top traders no matter how much certain funds switch over to “black box” systems.

A Day in the Life of a Quant

Q: Thanks for weighing in there. I know readers have asked a lot of questions on whether or not certain roles will still exist going forward.

So what’s it like to work at a quant fund? How are the hours? What’s a typical day like?

A: The hours vary; it’s probably around 45-65 hours per week on average depending on what’s going on in the market.

The total entry-level compensation is between $200K and $300K USD.

Compensation thereafter depends on the individual’s and firm’s performances. It could stay the same or potentially go up to the millions.

Q: I just want to pause for a minute so everyone can appreciate the sheer amount of cash you just mentioned: $200K – $300K USD for entry-level positions.

But going back to my actual question, what’s a typical day like for you in terms of schedule and work tasks?

A: Someone in a quant role at a hedge fund might complete the following tasks each day:

Idea Generation – They’ll read academic research, attend conferences and talk to each other to generate ideas; then they’ll gather data and write programs to test their ideas.

Turn Ideas Into Trades – They’ll build models to turn their ideas into trades and will monitor the performance and risk of their trades, tweaking their models as necessary.

Get Better Trades – They’ll talk to traders, dealers, brokers, and so on to get better trading terms, lower transaction fees, and other “concessions” that make it easier to trade profitably.

Fundraising and Interviewing – These tasks probably take up less time than the rest, but occasionally quants will help with speaking to new investors to raise money and with interviewing new candidates for open quant positions.

You don’t have much of a “set schedule” because it really depends on what’s going on in the market – it’s not quite as crazy as the wildly unpredictable investment banking hours, but I’ve definitely seen people here stay late and work weekends.

Q: For readers who want to get into a quant hedge fund, how would you recommend that they start? Are internships important? Are PhD’s important? What prerequisites are there to get into a top quant fund?

A: My #1 recommendation is to develop a strong understanding of statistics and some programming skills.

Q: Right, I think everyone knows that those skills are required, but what about getting the interview in the first place?

A: There are basically 3 ways to get interviews at quant funds: go through on-campus recruiting (not many funds participate, even at top schools), get friends at hedge funds refer you, or go through headhunters.

It’s just like the networking process for any other role: there’s a certain amount of randomness and luck involved, and you have to grind it out until you start landing interviews.

So if you’re a college freshman and you’re sure that you want to go into quant finance, you should pick a major that develops rigorous thinking and communication skills and one where you’ll work with data and do some programming.

Q: So if you’re a number-crunching machine who can answer brainteasers in your sleep, but you hate talking to people or interacting with carbon-based life-forms, quant roles might be for you.

A: Haha, well, that’s the non-PC way to put it I suppose. I would point out that you still have to interact with your team and communicate your ideas even if you’re in a quant role that’s more technically intense.

So it’s not like you can just sit and hibernate in a cave and never talk to people.

The difference is that your job isn’t dependent on calling people, developing relationships, thinking quickly on your feet, and cutting deals as it would be in the senior levels of banking and PE.

Q: What if you follow the quant route but end up not getting a quant job? What “Plan B” options are there in case you don’t get into your dream hedge fund?

A: Work for technology companies or anywhere where data analysis is important – for example, insurance companies, credit card companies, or social networks that have lots of user-generated data.

Your goals should be to learn statistics very well, gain market knowledge, and make money in the process.

Interview Drill-Down

Q: You mentioned before how they don’t care too much about “fuzzy factors” in quant interviews. What types of questions can you expect, beyond the general categories of “lots of math and brainteaser questions”?

A: Interview questions for traders within quant shops test your understanding of market events and intuition for asset pricing models. For example, they might ask you what typically happens to the US mortgage market when treasury bonds rally.

Other likely questions include those based on probability, statistics, and optimization.

One really common probability question in quant interviews is the “Birthday Problem”: what is the probability that two people in a room of 20 have the same birthday?

Another common quant interview question is the Monty Hall problem: you’re at a game show and there are three doors. Behind one door is a gift, and there’s nothing behind the other two doors.

You randomly pick a door, and the show’s host, who knows where the gift is, picks another door and opens it, revealing that there’s nothing behind it.

At this point, you’re given a choice to stick with your original pick or switch to the other door. Should you switch?

Q: So these questions have clear, correct answers, even though they may be counter-intuitive.

The Monty Hall one is interesting because it is in your benefit to switch, even though some people just can’t understand intuitively why that’s the case.

A: Yeah, the questions tend to be mathematically complex or counter-intuitive, or both.

So you need to think out loud in front of your interviewer so they can get a sense of what’s going through your mind. They can also help steer you in the right direction. Simply sitting there thinking in silence won’t help.

About the Author

Zeke Lee is a Stanford graduate and former management consultant with Booz & Company and derivatives trader on Wall Street. He founded GMAT Pill, a top-rated online GMAT Prep course designed for busy working professionals who want to study less and score more.

Comments

I’m a current student at Carnegie Mellon University interested in quantitative finance.

CMU’s (MSCF) Masters in Computational Finance degree is generally considered the best in the world, but there is also an undergraduate equivalent: the BSCF. I’m planning to participate in this program, but I have a few questions.

Would an undergraduate graduating from this program be able to expect the same starting compensation (your figure of $200-$300k) in a quantitative analyst role? Would you happen to know how well regarded such an undergraduate program would be (it being a CMU program, regardless of it being undergrad)?

Also, a figure that I’ve often heard quoted on the internet regarding starting salaries for quants is 100kish, which is far below the 2-300k figure you used above. Would this imply that standard bonuses for quants are 100%+ their base salary? What is the general salary-bonus breakup for quantitative analysts?

You will probably not start at that high a total compensation right out of undergrad. I don’t know that program, but it is at CMU, so I imagine it is well-regarded. The $100K figure probably does not include a bonus, but the figures quoted above assume some work experience prior to the quant fund.

Thank you for your response. I suppose that that level of compensation right out of undergrad was too good to be true. I understand that M&I is mostly focused on traditional finance — IB, PE, and HF in particular. Would you recommend a similar site more focused on quantitative/computational finance or a place where I can learn more about that industry?

I am a junior at a top target, and am a very strong programmer, but mediocre at math and stats. I have internship offers at Google and Facebook, but their pro-rated compensation is a little over half of what you suggested is available for new grad quant roles. If I am hired as a software engineer/developer at these quant funds for full time, should I also expect a 200-300k starting salary? Given my background, what would be the most lucrative role for me to take in finance?

You will probably not get that much as a starting salary. But yes, if programming is your strength, quant hedge funds do offer the most lucrative compensation (and should probably still be more than Google/Facebook).

I’m currently enrolled in a Financial Engineering program, by the time I graduate I will have R and MATLAB under my belt. The program has just started having little or no programming experience do you suggest start learning C++?? I’ve heard from ppl that you should know C++ really well to put it on your resume or else there’s no point in it.

I have learned a bit of python would you suggest getting good at python rather than start learning C++ in the next 8-9 months?

Any thoughts on Machine Learning as part of data analysis at quant hedge funds?

Or maybe I should just concentrate more on Statistics and get expert level skill in R…

Hello Brian,
Thank you for the informative post. I am currently an undergraduate senior majoring in math and finance. I am interested in working as a quant analyst for a low-frequency quant hedge fund. Currently I am contemplating on applying for a PhD. My question is what kind of a PhD would be most applicable for this role. I am not good at computer science, so I am thinking of either choosing statistics, mathematics or engineering. Furthermore, within these fields is there a more desirable path than others: for instance, applied math over pure math, or operations research over statistics, or financial math over financial engineering. Thank you so much for the help.
Best of luck,
Kristian

This is an awesome post, wish I came upon it earlier! I am currently doing a masters in statistics at stanford university, would you know how I can obtain interviews for quant funds? also, would a masters in statistics be enough to get my foot in the door or should I aim for a PHD? Thanks!

Brian,
Great website. I use it all the time and I purchased your interview guide. I am currently an applied mathematics sophomore at Georgia tech and I am really wanting to go into quant hedge funds. I am interning at a wealth management firm over the summer, but I want to know what I can do before I graduate to more easily get a full time spot in buy side ideally hedge funds.
Thanks,
Ben

Biggest thing is to network extensively, come in with great stock pitch / trade ideas, and get an internship at a fund that uses a highly relevant strategy… but it will be really tough regardless to start there out of undergrad.

“The importance of a human trader increases as assets that people trade become more complex. It decreases as more information becomes quantifiable and people better understand how to price and predict these assets.”

1. What are some “complex” assets where human traders are still important?

2. Similarly, what are some “less complex” assets where human traders are being replaced by machines/algorithms to a large extent?

So would a major in applied math/economics be less desirable to a quant fund than let’s say a computer science/statistics major? Or are all math majors looked at more or less the same, just the work tends to be more stat-heavy? Also I’m guessing that a Master’s at the very least is mandatory to be recruited as a quant?

A major in applied math is ok but for economics, you would need to demonstrate heavy use of statistics (econometrics?) and programming. Either way, most quant funds will wait for hires who have advanced degrees, at least master’s degree or PhD degree.

That said, it’s not unheard of for graduates with a bachelor’s degree to get an interview at these megafunds. It’s possible but passing the interview is rigorous and demonstrated practice of “hard core” quant is more common with advanced degree candidates.

There are some problems with this type of trading strategy. Statistical approaches rely on historical data, but analysts have no guarantee that past correlation values will continue in the future. There are also type I and type II errors. As a result, it would be interesting to know how much leverage these quant hedge funds, such as Renaissance, use. Let’s hope it’s not near the level of LTCM.

The interviewee could not go into details on those types of specifics. From what I understand, Renaissance has very restrictive non-compete and confidentiality agreements, so even former employees haven’t disclosed much of anything after leaving and no one really knows the specifics of their strategies.

I just wanted to comment on the Monty Hall problem, that it doesnt matter if you switch. There is a 50/50 chance.
Your previous selection does not affect yor future outcome.

Let’s see it this way… if you had 2 doors, one of them with a gift and nothing behind the other door.
What are your chances of winning the gift? 50 and 50 right? That’s undisputable.

So you pick one door and they are about to reveal your result. There is still a 50% chance that you have picked the gift.

Would it matter if they told you that there were 100 doors before, and it turns out that other person also picked the same door you just picked and these 2 doors are what is left of the 100 doors?
No, you still have your 50%

The other person HAD 1% of picking the gift and 99% of losing.

So, whichever door you or he picks NOW, you still have 50/50.

You might have the gift or not, there are 2 possible outcomes.

In the same way, the other person that had previously selected the same door that you just did, he now has 50/50, regarless of how many times they had offered him to switch before (out of the 100 doors.)

If there are 100 doors, the probability of picking the wrong door initially is 99%. Thus, if the host open 98 empty doors, switching would move your probability of picking the right door from 1% to 99%, as the probability of picking the wrong door initially, is 99%

If you’d like some more practice with probability and/or combinatorics – the kind of thinking that some quant interviews put you through, we put together a lesson on the topic of Poker Probability here:

The only way that you will lose by switching is if you initially pick the door which has the gift behind it. What’s the chance of that? If you have 3 doors, it’s 1/3. The events are mutually exclusive. So if you have a 1/3 chance of picking the door with the gift initially, you have a 1/3 chance of losing if you switch. Thus, you have a 2/3 chance of winning if you switch.

Great post again guys. You should have gotten the interviewee for his reply to the attacks made on just about everything quant related. From Nassin Talebs Black Swan to Deidre Mccloskey saying that statistical significance was dead, etc… RenTech also seems like an anomaly, the only quant fund I’ve read about that hasn’t blown up in the long run…

similar question, I work in a BB as a pricing analyst, I aced my math SAT, but was a total mess in high school and ended up at a non-target, I started in tech and worked my way into the front office, but now I want to connect to my math roots and join a quant hedge fund. Any suggestions for a transition like this?

I thought I was gonna find out that because they charge 5 and 44 the return’s not all that great. 13.62% is decent but over the past 30 years (excluding the most recent 5), I feel that it’s something quite a few moderately-intelligent investment professionals could have done with a a little luck on their side.

1. Specific languages don’t matter that much for most programming roles because they assume you can pick up anything if you’re smart and good at programming. Not sure which one is preferred for quant roles, but I believe you’ll see C/C++, Java, and C# and maybe more.

2. It helps a lot for quant and programming roles, but less for pure trading roles I believe (though it may help there as well).

Matlab is very important (or R), and recently I was asked about Python. And you can think of practically anything even obscure and weird ones like K, F#, Haskell, Root and so on. Most of these places are very intellectually driven so they will expect you to pick up any new knowledge fairly quickly and enthusiastically.

Probably you should be familiar with databases as well. The teams are usually small and work in isolation so top down approach is required. You are going to do everything from data cleaning to trade execution.